Project Summary/Abstract New treatments have been revolutionary in improving outcomes over the last 30 years, yet cardiovascular disease still exerts a $320B annual burden on the US economy. Increasing evidence is showing that Coronary CT Angiography (CCTA) may be an ideal modality to fill gaps in understanding the extent and rate of progression coronary artery disease. But despite the apparent promise of CCTA, there are barriers that prevent realizing the improvement that it theoretically provides. Currently available solutions do not overcome the barriers ? a new approach is needed. Elucid Bioimaging has developed an image analysis software product vascuCAP (CAP stands for Computer Aided Phenotyping) to accurately quantify structural and morphological characteristics of plaque tissues linked to plaque rupture vulnerability. Fundamental to our approach is validated, objective quantitative accuracy; vascuCAP enjoys the most robust and well documented analytic validation of any plaque morphology software available. vascuCAP is the only system to mitigate specific issues in CT reconstruction known to effect accurate measurement of atherosclerotic plaque composition in routinely acquired CTA; it is the only system to effectively leverage objective tissue characterization validated by histology across multiple arterial beds; it achieves an effective resolution with routinely acquired CTA in the same ballpark as IVUS VH, based on solid mathematics principles that respect the Nyquist-Shannon sampling theorem; and it innovates by novel reporting that expresses the findings in a manner that fits efficiently into existing clinical workflows. vascuCAP has been implemented in a client-server model supporting SaaS. Working from our strong current device clearances, this research strategy is developed based on approved meeting notes from the FDA pre-submission process Phenotype classification claims to be cleared through direct De Novo pathway on the basis of accurately determining the class from in vivo CTA data relative to pathologist annotation on ex vivo specimen data. Risk prediction claims: validate ability to predict adverse events at one year, adding the IFU according to the direct De Novo pathway, One does not strictly depend on the other.This proposal is innovative in dealing with two fundamental limitations of the application of artificial intelligence and deep learning to the analysis of atherosclerosis imaging data. This proposal maximizes use of available retrospective data while putting in place the necessary structure for prospective validation and scale up. This proposal further develops vascuCAP as a tool that may reduce cost and length of clinical trials. While out of scope for this grant, it is important to also note that vascuCAP is innovative in its ability to support multi-scale modeling across cellular/molecular-level analyses and macroscopic manifestation. Also, vascuCAP?s quantitative ability makes it ideal for analysis of more advanced CT imaging protocols. These attributes complement and support the proposed objectives.